Egocentric Visitors Localization in Natural Sites

Localizing visitors in natural environments is challenging due to the unavailability of pre-installed cameras or other infrastructure such as WiFi networks. We propose to perform localization using egocentric images collected from the visitor’s point of view with a wearable camera. Localization can be useful to provide services to both the visitors (e.g., showing where they are or what to see next) and to the site manager (e.g., to understand what the visitors pay more attention to and what they miss during their visits). We collected and publicly released a dataset of egocentric videos asking 12 subjects to freely visit a natural site. Along with video, we collected GPS locations by means of a smartphone. Experiments comparing localization methods based on GPS and images highlight that image-based localization is much more reliable in the considered domain and small improvements can be achieved by combining GPS- and image-based predictions using late fusion.